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[1] Chen Chen, Pan Lei, Kwang Y. Lee, et al. Min-max fuzzy model predictive tracking controlof boiler-turbine system for ultra-supercritical units [J]. Journal of Southeast University (English Edition), 2021, 37 (1): 42-51. [doi:10.3969/j.issn.1003-7985.2021.01.006]
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Min-max fuzzy model predictive tracking controlof boiler-turbine system for ultra-supercritical units()
超超临界机组机炉协调系统的min-max模糊模型预测跟踪控制
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
37
Issue:
2021 1
Page:
42-51
Research Field:
Computer Science and Engineering
Publishing date:
2021-03-20

Info

Title:
Min-max fuzzy model predictive tracking controlof boiler-turbine system for ultra-supercritical units
超超临界机组机炉协调系统的min-max模糊模型预测跟踪控制
Author(s):
Chen Chen1;  2;  Pan Lei1;  2;  Kwang Y. Lee3
1Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, China
2School of Energy and Environment, Southeast University, Nanjing 210096, China
3Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76798-7356, USA
陈琛1;  2;  潘蕾1;  2;  Kwang Y. Lee3
1东南大学能源热转换及其过程测控教育部重点实验室, 南京 210096; 2东南大学能源与环境学院, 南京 210096; 3Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76798-7356, USA
Keywords:
ultra-supercritical boiler-turbine system;  T-S model;  min-max model predictive control;  output tracking;  linear matrix inequality
超超临界机组机炉协调系统;  T-S模型;  min-max模型预测控制;  输出跟踪;  线性矩阵不等式
PACS:
TP391
DOI:
10.3969/j.issn.1003-7985.2021.01.006
Abstract:
To improve the control performance of nonlinear ultra-supercritical(USC)thermal power units, an improved min-max fuzzy model predictive tracking control(FMPTC)strategy is proposed. First, a T-S fuzzy model is established to approximate the dynamics of the nonlinear boiler-turbine system. Then, based on an extended fuzzy model containing state variables and output variables, a min-max FMPTC is derived for output regulation while ensuring the closed-loop system stability and the inputs in their given constraints. For greater controller design freedom, the developed controller adopts a new state- and output-based objective function. In addition, the observer estimation error is regarded as a bounded disturbance, ensuring the stability of the entire closed-loop control system. Simulation results on a 1 000 MW USC boiler-turbine model illustrate the effectiveness of the proposed approach.
为了提高非线性超超临界火电机组的控制性能, 提出了一种改进的min-max 模糊模型预测跟踪控制.首先, 建立了T-S模糊模型来近似非线性机炉协调系统的动态特性.然后, 基于包含状态变量和输出变量的扩展模糊模型, 在保证闭环系统稳定性和输入在给定约束的同时, 推导了min-max 模糊模型预测跟踪控制用于输出调节.为了获得更大的控制器设计自由度, 开发的控制器采用了新的基于状态和输出的目标函数.此外, 观测器的估计误差被视为一个有界干扰, 保证了整个闭环控制系统的稳定性.在一个1 000 MW机炉协调系统模型上的仿真结果验证了提出方法的有效性.

References:

[1] Pan L, Shen J, Wu X, et al. Improved internal-model robust adaptive control with its application to coordinated control of USC boiler-turbine power units in flexible operations[J].International Journal of Systems Science, 2020, 51(4): 669-686. DOI:10.1080/00207721.2020.1737267.
[2] Chen C, Pan L, Liu S J, et al. A sustainable power plant control strategy based on fuzzy extended state observer and predictive control[J].Sustainability, 2018, 10(12): 4824. DOI:10.3390/su10124824.
[3] Pan L, Luo J, Cao C Y, et al. L1 adaptive control for improving load-following capability of nonlinear boiler-turbine units in the presence of unknown uncertainties[J].Simulation Modelling Practice and Theory, 2015, 57: 26-44. DOI:10.1016/j.simpat.2015.05.012.
[4] Kong X B, Liu X J, Lee K Y. An effective nonlinear multi-variable HMPC for USC power plant incorporating NFN-based modeling[J]. IEEE Transactions on Industrial Informatics, 2016, 12(2): 555-566. DOI:10.1109/TII.2016.2520579.
[5] Moon U C, Lee K Y. Step-response model development for dynamic matrix control of a drum-type boiler-turbine system[J]. IEEE Transactions on Energy Conversion, 2009, 24(2): 423-430. DOI:10.1109/TEC.2009.2015986.
[6] Hou G L, Xi Y, Liu J B, et al. Simulation research of the multi-variable generalized predictive control in 500 MW unit plant coordinated control system[C]//The 2011 International Conference on Advanced Mechatronic Systems. Zhengzhou, China, 2011: 196-201.
[7] Zhang F, Shen J, Li Y G, et al. Nonlinear model predictive control of ultra-supercritical once through boiler-turbine unit[C]//2015 Chinese Automation Congress. Wuhan, China, 2015: 2194-2198. DOI:10.1109/CAC.2015.7382868.
[8] Liu X J, Guan P, Chan C W. Nonlinear multi-variable power plant coordinate control by constrained predictive scheme[J]. IEEE Transactions on Control Systems Technology, 2010, 18(5): 1116-1125. DOI:10.1109/TCST.2009.2034640.
[9] Liu X J, Jiang D, Lee K Y. Quasi-min-max fuzzy MPC of UTSG water level based on off-line invariant set[J]. IEEE Transactions on Nuclear Science, 2015, 62(5): 2266-2272. DOI:10.1109/TNS.2015.2466658.
[10] Cui J H, Chai T Y, Liu X J. Deep-neural-network-based economic model predictive control for ultrasupercritical power plant[J]. IEEE Transactions on Industrial Informatics, 2020, 16(9): 5905-5913. DOI:10.1109/TII.2020.2973721.
[11] Ding B C. Dynamic output feedback predictive control for nonlinear systems represented by a Takagi-Sugeno model[J]. IEEE Transactions on Fuzzy Systems, 2011, 19(5): 831-843. DOI:10.1109/TFUZZ.2011.2147320.
[12] Ding B C, Ping X B. Output feedback predictive control with one free control move for nonlinear systems represented by a Takagi-Sugeno model[J]. IEEE Transactions on Fuzzy Systems, 2014, 22(2): 249-263. DOI:10.1109/TFUZZ.2013.2251637.
[13] Kothare M V, Balakrishnan V, Morari M. Robust constrained model predictive control using linear matrix inequalities[J]. Automatica, 1996, 32(10): 1361-1379. DOI:10.1016/0005-1098(96)00063-5.
[14] Tseng C S, Chen B S, Uang H J. Fuzzy tracking control design for nonlinear dynamic systems via T-S fuzzy model[J]. IEEE Transactions on Fuzzy Systems, 2001, 9(3): 381-392. DOI:10.1109/91.928735.
[15] Wu X, Shen J, Li Y G, et al. Fuzzy modeling and stable model predictive tracking control of large-scale power plants[J]. Journal of Process Control, 2014, 24(10): 1609-1626. DOI:10.1016/j.jprocont.2014.08.007.
[16] Zhang T J, Feng G, Zeng X J. Output tracking of constrained nonlinear processes with offset-free input-to-state stable fuzzy predictive control[J]. Automatica, 2009, 45(4): 900-909. DOI:10.1016/j.automatica.2008.11.016.
[17] Zhang F, Wu X, Shen J. Extended state observer based fuzzy model predictive control for ultra-supercritical boiler-turbine unit[J]. Applied Thermal Engineering, 2017, 118: 90-100. DOI:10.1016/j.applthermaleng.2017.01.111.
[18] Kong L, Yuan J Q. Generalized discrete-time nonlinear disturbance observer based fuzzy model predictive control for boiler-turbine systems[J]. ISA Transactions, 2019, 90: 89-106. DOI:10.1016/j.isatra.2019.01.003.
[19] Wu S, Jin Q B, Zhang R D, et al. Improved design of constrained model predictive tracking control for batch processes against unknown uncertainties[J]. ISA Transactions, 2017, 69: 273-280. DOI:10.1016/j.isatra.2017.04.006.
[20] Zhang R D, Wu S, Cao Z X, et al. A systematic min-max optimization design of constrained model predictive tracking control for industrial processes against uncertainty[J]. IEEE Transactions on Control Systems Technology, 2018, 26(6): 2157-2164. DOI:10.1109/TCST.2017.2748059.
[21] Liu J Z, Yan S, Zeng D L, et al. A dynamic model used for controller design of a coal fired once-through boiler-turbine unit[J]. Energy, 2015, 93: 2069-2078. DOI:10.1016/j.energy.2015.10.077.
[22] Ding B C, Xi Y G, Li S Y. A synthesis approach of on-line constrained robust model predictive control[J]. Automatica, 2004, 40(1): 163-167. DOI:10.1016/j.automatica.2003.07.007.

Memo

Memo:
Biographies: Chen Chen(1991—), male, Ph. D. candidate; Pan Lei(corresponding author), female, doctor, professor, panlei@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No. 51936003).
Citation: Chen Chen, Pan Lei, Kwang Y. Lee.Min-max fuzzy model predictive tracking control of boiler-turbine system for ultra-supercritical units[J].Journal of Southeast University(English Edition), 2021, 37(1):42-51.DOI:10.3969/j.issn.1003-7985.2021.01.006.
Last Update: 2021-03-20